AquaFed: Leveraging Federated Learning for Real-Time Schistosomiasis Prevention Through Water Quality Monitoring
Bibliographic record
Abstract
Schistosomiasis, a waterborne parasitic disease caused by trematode flatworms of the genus Schistosoma, transmitted through skin contact with freshwater containing infectious larvae released by specific snail hosts, remains a critical public health concern in endemic regions, where early detection and intervention are vital for effective disease prevention. This work presents AquaFed, a decentralized system for real-time monitoring and forecasting of water quality parameters and freshwater snail populations—key indicators in schistosomiasis transmission. AquaFed leverages Federated Learning (FL) in combination with Long Short-Term Memory (LSTM) networks to enable predictive modeling across distributed IoT sensor kits. By training models locally and sharing only model updates, AquaFed significantly reduces communication bandwidth during both training and inference, while stabilizing local updates and accelerating global convergence, in addition to enabling scalable deployment in resource-constrained environments. Experimental evaluations conducted in a schistosomiasis-endemic region of Burkina Faso demonstrate that AquaFed achieves forecasting performance on par with traditional centralized learning approaches, while reducing the utilization of the communication bandwidth. These findings underscore the potential of AquaFed as a robust, communication-efficient platform for real-time risk assessment and proactive schistosomiasis control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".